TL;DR: A 60% win rate trader can lose money. A 35% win rate trader can be very profitable. The number that matters is expectancy — your average gain per trade across all trades. TJL Pro calculates this automatically from your journal. Here is how it works.
Why Win Rate Is the Most Misunderstood Stat in Trading
Every new trader asks: "What's your win rate?" It sounds like the right question. It isn't.
Win rate is the percentage of your trades that close in profit. A 60% win rate sounds good. But consider:
- **Trader A**: 60% win rate. Average win ₹2,000. Average loss ₹5,000.
- **Trader B**: 38% win rate. Average win ₹8,000. Average loss ₹3,000.
Win rate without reward-to-risk ratio is noise. The stat that matters is expectancy.
The Three Numbers Every Trader Must Know
1. Win Rate (W%)
Formula: ``` Win Rate = (Number of Winning Trades ÷ Total Trades) × 100 ```
Example: You placed 80 trades last month. 44 closed in profit. Win Rate = (44 ÷ 80) × 100 = 55%
2. Average Risk-Reward Ratio (RR)
Formula: ``` Average RR = Average Winning Trade Size ÷ Average Losing Trade Size ```
Example: Your 44 winning trades averaged ₹3,200 profit. Your 36 losing trades averaged ₹2,400 loss. Average RR = ₹3,200 ÷ ₹2,400 = 1.33
3. Trade Expectancy (EV)
Formula: ``` Expectancy = (Win Rate × Average Win) − (Loss Rate × Average Loss) ```
Example (continuing above): Expectancy = (0.55 × ₹3,200) − (0.45 × ₹2,400) = ₹1,760 − ₹1,080 = +₹680 per trade
If you place 80 trades per month with +₹680 expectancy, your expected monthly P&L is ₹54,400. This is now a planning number, not a hope number.
R-Multiple: The Professional's Unit
Professionals don't talk in rupees when measuring performance — they talk in R-multiples. R is your risk per trade (the amount you'd lose if your stop-loss is hit).
If you risk ₹2,000 per trade: - A trade that makes ₹4,000 = +2R - A trade that loses ₹2,000 = −1R - A trade that makes ₹1,000 = +0.5R
Why this matters: R-multiples let you compare performance across different position sizes and market conditions. A month where you risked ₹1,000 per trade and a month where you risked ₹3,000 per trade look completely different in rupee P&L — but identical in R-multiples.
Expectancy in R: ``` R Expectancy = Average R per trade across all trades ```
A system with +0.4R expectancy means: for every ₹1 risked, you make ₹0.40 on average. At 60 trades per month risking ₹2,000 each, that's 60 × 0.4 × ₹2,000 = ₹48,000 expected monthly profit.
The Minimum Win Rate for Profitability at Each RR Ratio
This table shows the minimum win rate you need to break even (expectancy = 0) at various reward-to-risk ratios:
| R:R Ratio | Min Win Rate to Break Even |
|---|---|
| 1:1 | 50% |
| 1.5:1 | 40% |
| 2:1 | 33% |
| 3:1 | 25% |
| 4:1 | 20% |
Most intraday traders target a 1.5:1 to 2:1 ratio. If you're hitting 2:1, you only need to win 34% of your trades to be profitable. If you're winning 50% at 2:1, your expectancy is very strong.
How to Calculate These Numbers From Your Zerodha/Upstox Data
Step 1: Export your tradebook from your broker (Zerodha → Reports → Tradebook → Download CSV, or Upstox → Reports → Trade History).
Step 2: Import into TJL Pro. The system automatically calculates: - Win rate (overall and by setup type) - Average win and average loss - Expectancy per trade - R-multiple per trade (once you set your stop-loss on each trade) - Win rate by day of week, session time, market condition, and setup tag
Step 3: Look at your expectancy by setup type. This is where the insight lives.
You might find: - Opening range breakout trades: Win rate 48%, expectancy +₹1,200 → keep trading this - Gap-fill trades: Win rate 29%, expectancy −₹800 → stop trading this immediately
Common Mistakes When Analysing Your Stats
Mistake 1: Too small a sample
A 20-trade sample size is not statistically meaningful. You need at least 50-100 trades before drawing conclusions. A bad week can look like a "bad system" on 20 trades; on 100 trades, the signal is clearer.
Mistake 2: Lumping all setups together
Your overall win rate hides the truth. A 52% win rate could be +1.5R expectancy from your best setup and −0.8R from your worst — blended together to look "fine." Always segment by setup.
Mistake 3: Ignoring slippage and brokerage
Your gross P&L and net P&L can differ significantly, especially for high-frequency intraday traders. NSE F&O has STT, exchange charges, GST, and Zerodha brokerage. TJL Pro calculates net P&L from your actual tradebook.
Mistake 4: Measuring without acting
Stats are only useful if they change your behaviour. Set a rule: "If any setup shows negative expectancy over 60+ trades, I stop trading it." Measure, decide, act.
What Good Performance Numbers Look Like
For reference — these are rough benchmarks for Indian retail traders:
| Metric | Struggling | Developing | Consistent |
|---|---|---|---|
| Win Rate | <35% | 35-55% | 45-60%+ |
| Avg RR | <1.0 | 1.0-1.5 | 1.5-2.5+ |
| Expectancy | Negative | +₹0 to +₹500 | +₹500 to ₹2,000+ |
| Max Drawdown | >25% | 10-20% | <10% |
These aren't targets — they're diagnostics. A 35% win rate with 3:1 RR is excellent. A 65% win rate with 0.5:1 RR is a losing system.
TJL Pro's Analytics Dashboard
TJL Pro automatically shows you all of this from your journal data:
- **Win rate** — overall, by instrument, by setup tag, by time of day, by day of week
- **Expectancy** — per trade, per setup, trending over time
- **P&L calendar** — see your green and red days in a heat map
- **Equity curve** — your running account balance over time
- **Mistake frequency** — how often you break your own rules, and what it costs
Import your Zerodha, Upstox, or Angel One tradebook and your analytics are ready in minutes.
See your real numbers → Start free
Related Reading
- [Why 90% of Indian F&O traders lose money — and what to do about it](/blog/why-fo-traders-lose-money-india)
- [F&O trading journal: track NIFTY and BANKNIFTY options correctly](/blog/fo-trading-journal-india)
- [How to keep a trading journal that actually improves your trading](/blog/how-to-journal-trades)
*Last updated: May 2026*